Evidence map›Paper›PMID 39917554›Full record

ArticleThe British journal of cardiology2024

Artificial intelligence in heart valve disease: diagnosis, innovation and treatment. A state-of-the-art review.

Paul Bamford, Amr Abdelrahman, Christopher J Malkin, Michael S Cunnington, Daniel J Blackman, Noman Ali

Abstract read
In one paragraph

Article in The British journal of cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Review
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  6. Review
  7. Ethnic variation in aortic root anatomy and prosthesis sizing in TAVI: a UK matched study.European heart journal. Imaging methods and practice · 2025
    Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Paul BamfordInterventional Fellow.
Amr AbdelrahmanInterventional Fellow.
Christopher J MalkinInterventional Cardiologist.
Michael S CunningtonInterventional Cardiologist.
Daniel J BlackmanInterventional Cardiologist.
Noman AliInterventional Cardiologist Leeds Teaching Hospitals, Great George Street, Leeds, West Yorkshire, LS1 3EX.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, artificial intelligence (AI) has been used to improve the precision of valvular heart disease diagnosis and treatment. It has the ability to identify and risk stratify patients with valvular heart disease and holds promise in improving the innovation of new treatments through shorter, safer and more effective clinical trials. AI can help to guide the treatment of patients with valvular heart disease, by aiding in optimal device selection for transcatheter valvular interventions and, potentially, predicting the risk of specific complications. This review article explores the various potential applications of AI in the diagnosis and treatment of valvular heart disease in more detail.

Indexed as

artificial intelligencecomputer simulationstranscatheter aortic valve implantation (TAVI)valvular heart disease

Identifiers

PMID39917554
PMCPMC11795922

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.